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An Image enhancement algorithm based on Gaussian weighted bilateral filtering and retinex theory

机译:基于高斯加权双边滤波和retinex理论的图像增强算法

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An image enhancement algorithm based on Gaussian weighted bilateral filtering and Retinex theory is proposed to solve the disadvantages about halo, grey and noise amplification phenomenons of single-scale Retinex algorithm. First, the illumination images are estimated with Gaussian weighted bilateral filtering and their influence on visual effect is removed, which can overcome the halo phenomenon effectively. Then the contrast in low contrast regions is enhanced with piecewise linear transformation, which can improve the grey phenomenon of enhanced images. Finally, threshold values are determined by Otsu algorithm to identify the dark areas of images, and the noise in dark areas is removed by bilateral filtering. Experiments show that the new algorithm can remove halo phenomenon, enhance the contrast of images, get more abundant image information and overcome the noise amplification phenomenon, which is better than the conventional single-scale Retinex algorithm and the Retinex algorithm based on bilateral filtering.
机译:提出了一种基于高斯加权双边滤波和Retinex理论的图像增强算法,以解决单尺度Retinex算法在光晕,灰度和噪声放大现象上的缺点。首先,利用高斯加权双边滤波对照明图像进行估计,消除对视觉效果的影响,可以有效克服光晕现象。然后通过分段线性变换来增强低对比度区域中的对比度,这可以改善增强图像的灰度现象。最后,通过Otsu算法确定阈值以识别图像的暗区,并且通过双边滤波去除暗区中的噪声。实验表明,该算法可以消除光晕现象,增强图像对比度,获得更丰富的图像信息,克服了噪声放大现象,优于传统的单尺度Retinex算法和基于双边滤波的Retinex算法。

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